Contrastive learning is a commonly used technique to learn with augmented graph views in pairs [1]}. It aims to increase
similarity scores for positive pairs and decrease similarity scores for negative pairs.
However, existing graph contrastive learning methods mainly adopt static graph representations [1]}, [3]}, [4]}, where node-level interaction across graphs is not explicitly modeled
in this process. As a comparison, given a pair of graph views, we first apply the neural graph matching technique (Section REF ) to characterize inter-graph interaction, and then construct the contrastive loss based on the adaptive graph representations.
